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Futures: Deep Neural Networks

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Be introduced to the Artificial Neural Network (ANN) and develop a Deep Neural Network (DNN) framework from scratch, then apply it to classification and recommendation systems.

 

Machine Learning models constructed with Deep Neural Networks have gained tremendous momentum over the years, and the complexities associated with their design, training, and deployment is a valuable skill for today’s Machine Learning Engineer.
 
Students completing this third course in the Machine Learning certificate program will gain a working knowledge of the simplest Artificial Neural Network (the Perceptron) and build up a functional framework from scratch in Python to implement feed forward, loss minimization, back propagation, and optimizations.  The framework will then be applied to solving complex mathematical functions as well as to classification and recommendation systems, and later extended to specialized algorithms such as Convolutional Neural Networks.

What You Will Learn

 
  • Implementation and applications of simple a Artificial Neural Network (ANN), i.e. the Perceptron.
  • Deep Neural Network (DNN) framework creation from scratch in Python.
  • Biological neuron model emulation with single and multi-layer Perceptrons.
  • Multi-node and multi-layer DNN’s applications to solve mathematical and classification problems.
  • Optimizations and Hyperparameters applied to DNNs.
  • Convolutional Neural Networks (CNN) applications to image processing.
Return to the Futures website here. 

Course Number: CSE-90161
Credit: 3.00 unit(s)

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